Adaptive Admittance Control of Human–Spacesuit Interaction for Joint-Assisted Exoskeleton Robot in Active Spacesuit

Abstract

To deal with the astronaut’s motion intention, as well as uncertainties in robotic dynamics, a human–spacesuit interaction (HSI) model is presented for the development of a joint-assisted exoskeleton robot in an active spacesuit using adaptive admittance control. Firstly, an adaptive RBF neural network control was designed for different astronauts, or the same astronauts in different states, which could be used to approximate the variable HSI model as a whole. Secondly, based on robust fuzzy control, the position inner loop of adaptive admittance control was designed to enhance the tracking effect for a given reference trajectory. When there is an interaction force between the active spacesuit and the wearer, the actual HSI force measured by the sensor transforms into the correction of the desired trajectory input, and the position inner loop tracks the corrected reference trajectory. The online estimation of stiffness is employed to assess the variable impedance property of a joint-assisted exoskeleton robot in an active spacesuit. Oxygen consumption decreased by 15.88% at most, which indicates that the proposed control method enables the wearer to effectively execute a simulated lunar sample collection mission with the joint-assisted exoskeleton robot.